[agents] [meetings] ETFA21 - Workshop on Planning and Control of Industrial Robots - CfP

Mon May 17 14:07:18 EDT 2021

Call for papers  (apologies for multiple posting)

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Workshop Title

Towards the factory of the future: advancements in planning and control 
of industrial robots

Organized and Co-chaired by

Marco Faroni, National Research Council of Italy, CNR-STIIMA

Alessandro Umbrico, National Research Council of Italy, CNR-ISTC

Manuel Beschi, University of Brescia

https://2021.ieee-etfa.org/solicited-workshops/ws1-towards-the-factory-of-the-future-advancements-in-planning-and-control-of-industrial-robots/

The workshop will be held during the 26th International Conference on 
Emerging Technologies and Factory Automation (ETFA 2021 - 
https://2021.ieee-etfa.org/)

Important Dates

Submission deadline: June 11th

Acceptance notification: July 7th

Deadline for final manuscripts: July 14th

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Aims and Objectives

***********

Industrial robots play a key role in industrial automation. Robotic arms 
populate shop-floors: they are used for pick-and-place, assembly, 
inspection, and many other tasks, to increase the throughput of 
productive processes and alleviate fatigue and risks of human workers. A 
huge research effort has been put into the reasoning, planning, and 
control of robotic manipulators. Nonetheless, industrial implementations 
often do not exploit at full the great advancements made in these 
fields. This workshop aims to discuss how recent developments in the 
planning and control of robot manipulators, on the one hand, and the 
synergetic integration with results from Artificial Intelligence, on the 
other, can advance the state of the art and be applied to real-world 
manufacturing processes. Among the many challenges in the field, the 
workshop will focus on the following trends that emerged in recent years:

  *

    Human-robot collaboration: collaborative robots are expected to play
    a key role in the factories of the future. The collaboration between
    humans and robots is supposed to combine the dexterity and reasoning
    ability of humans with the precision and continuity of robots.
    Current industrial solutions often lack smoothness and collaboration
    results to be discontinuous. This occurs at different
    decision-making levels. For example, implementations of safety rules
    according to safety standards (e.g., ISO-TS 15066) stop the robot as
    soon as human workers enter the robot workspace. Moreover, robot
    trajectories are often pre-computed and do not adapt to the system
    changes. Finally, ordering, scheduling, and assignment of tasks do
    not model human behaviors and preferences, resulting in poor
    dependability and jeopardizing the overall collaboration experience.
    Recent advances in task and motion planning addressed this issue in
    many several ways. Innovative methods have been developed to improve
    safety, ergonomics, and the efficiency of the process. Nonetheless,
    a well-established common paradigm is still to come.

  *

    Cognitive manufacturing: a central aspect concerning the integration
    of AI and Robotics in modern manufacturing scenarios is the
    enhancement of perception and reasoning capabilities of robotic
    solutions. AI technologies can indeed help to endow robot
    controllers with the  necessary cognitive capabilities to
    “understand” the state of human operators and the environment as
    well as contextualize robot behaviors accordingly. A collaborative
    robot would, for example, dynamically adapt its behaviors to known
    skills and monitored physiological state of human workers (e.g.,
    ergonomics, cognitive load, fatigue, etc.) in order to achieve a
    smooth and natural interaction. Such higher level of cognition is
    crucial to systematically include human-factors in the loop and
    really enable symbiotic, personalized and adaptive interactions
    between humans and robots.

  *

    Flexible manipulation in challenging scenarios: pick-and-place,
    sorting, and packaging can be efficiently automatized when they are
    required to manipulate objects with low variability (similar sizes
    and shapes) and they are performed in structured environments.
    However, when it comes to partially structured environments or
    high-variability, current industrial solutions usually fail because
    of a lack of flexibility and efficiency. Similarly, manipulation of
    large and/or deformable objects is still a hard task to perform with
    robotic manipulators. Examples are those draping processes required
    in automotive and aerospace (carbon-fiber manipulation) and in the
    textile industry. Despite these topics have been addressed for a
    long time by researchers, real-world implementations and successful
    case studies are rare and only recent research projects are trying
    to effectively automatize these processes. These new solutions
    should integrate vision, learning, and planning.

We invite researchers from both industry and academia to contribute to 
this workshop with papers on their recent advances in these fields, 
focusing on both theoretical methodology and industrial case studies.

Acknowledgement

***********

This workshop is partially supported by the EU funded project Sharework 
(H2020 Factories of the Future GA No. 820807) 
https://sharework-project.eu <https://sharework-project.eu/>.

Topics of interest (but not limited to)

***********

Applicants are expected to be conducting research in the field of 
planning and control of Industrial robots. Topics of interest include 
(but are not limited to):

  *

    Human-aware planning and execution in human-robot collaboration

  *

    Motion planning and control in dynamic environments

  *

    Long-term autonomy in human-robot collaborative scenarios

  *

    Manipulation of deformable/large objects

  *

    Combined task and motion planning

  *

    Multi-robot coordination and synchronization

  *

    Design and optimization of robotized workcells

  *

    Human-centered design of robotized cells

  *

    Safety and ergonomics of physical human-robot collaboration

  *

    Failure detection and recovery in HRC control systems

  *

    Evaluation methods for HRC workplaces and process (productivity,
    flexibility, etc.)

  *

    Vision and control of industrial robots for HRI applications

  *

    Novel Sensing and grasping technologies for HRI

  *

    Interfaces for real-time path and motion planning and collision
    avoidance

  *

    Case studies, experiments, ethics and outreach

Submissions

***********

Papers are limited to 8 double column pages.

They must comply with ETFA guidelines regarding formatting 
(https://www.ieee.org/conferences/publishing/templates.html) and must be 
submitted electronically in PDF format through the conference submission 
system:

http://submit.ieee-ies.org/submit/etfa21/

Accepted papers must be presented at the workshop in order to be 
included in the ETFA conference proceedings and will be published on 
IEEE Xplore.

Organisation Chairs

*******************

Marco Faroni

Institute of Intelligent Industrial Technologies and Systems for 
Advanced Manufacturing (STIIMA)

National Research Council (CNR), Italy

Alessandro Umbrico

Institute for Cognitive Science and Technologies (ISTC)

National Research Council (CNR), Italy

Manuel Beschi

Department of Industrial and Mechanical Engineering

University of Brescia, Italy

Program Committee

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Cosmin Copot, University of Antwerp, Belgium

Martina Lippi, University of ROMA TRE, Italy

Sotiris Makris, LMS, University of Patras, Greece

Andrea Orlandini, National Research Council of Italy (CNR-ISTC), Italy

Simone Pasinetti, University of Brescia, Italy

Nicola Pedrocchi, National Research Council of Italy (CNR-STIIMA), Italy

José Saenz, Fraunhofer IFF, Germany

Alberto Tellaeche, University of Deusto, Spain

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Dr. Alessandro Umbrico, PhD

National Research Council of Italy
Institute of Cognitive Sciences and Technologies

E-mail:     	alessandro.umbrico at istc.cnr.it
Linkedin:   	https://it.linkedin.com/in/alessandroumbrico
Researchgate:	https://www.researchgate.net/profile/Alessandro-Umbrico
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